A Sensorized Toy Car for Autism Screening Using Multi-Modal Features

Bijan Mehralizadeh, Bahar Baradaran, Shahab Nikkhoo, Pegah Soleiman and Hadi Moradi ()
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Bijan Mehralizadeh: School of Mechanical Engineering, University of Tehran, Tehran 1417614411, Iran
Bahar Baradaran: Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
Shahab Nikkhoo: School of EECS, University of Riverside, Riverside, CA 92521, USA
Pegah Soleiman: School of ECE, University of Tehran, Tehran 1417614411, Iran
Hadi Moradi: School of ECE, University of Tehran, Tehran 1417614411, Iran

Sustainability, 2023, vol. 15, issue 10, 1-10

Abstract: Early screening, which can lead to early diagnosis and intervention for children with autism (ASD), can significantly improve their life quality. Consequently, the observational process of the ASD diagnosis and the lack of experts make the technology-based ASD screening methods more demanding. On the other hand, early ASD screening based on behavioral patterns is one of the most reliable methods that could be accomplished by analyzing children’s playing patterns. Thus, in this paper, we present an extension of our initial sensorized toy car functionalities by adding shaft encoders to detect attention to details and interest in rotating objects in children with ASD. Using the two modalities to detect different ASD symptoms improved our screening accuracy by more than 10%. Having such a sensorized toy car, which can be used at homes or clinics for screening ASD without high expertise, can improve the life of children with ASD and their families by providing an initial screening for further evaluation. This is essential for sustainable development, especially considering the increased rate of children with ASD around the world.

Keywords: autism spectrum disorder (ASD); sensorized toy; machine learning; IoT (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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